Benjamin Eysenbach - Benjamin Eysenbach is currently a Resident at Google Brain, where he works on safe and autonomous reinforcement learning with Sergey Levine and Julian Ibarz. He received a BS in Math from MIT, where he also did research in computer vision with Antonio Torralba and Carl Vondrick. He has presented work at the NIPS 2017 Deep RL Symposium, ICLR, and ICML. [Website] [Google Scholar]

Surya Bhupatiraju - Surya Bhupatiraju is currently a Resident at Google Brain, where he works on better reinforcement learning algorithms with George Tucker, Shane Gu, and Sergey Levine, and on evaluation of generative models with Ian Goodfellow. He received a BS in computer science from MIT, where he spent time working on reinforcement learning with Josh Tenenbaum and Tejas Kulkarni and spent time working at MSR on neural program synthesis with Pushmeet Kohli, Abdelrahman Mohamed and Rishabh Singh. He has presented work at ICLR and ICML. [Google Scholar]

Shixiang (Shane) Gu - Shixiang (Shane) Gu is currently a PhD candidate at University of Cambridge and Max Planck Institute for Intelligent Systems, where he is jointly co-supervised by Richard E. Turner, Zoubin Ghahramani, and Bernhard Schoelkopf. He holds BASc. in Engineering Science from University of Toronto, where he completed this thesis with Geoffrey Hinton. His research interests span deep reinforcement learning, deep learning, robotics, approximate inference and causality, and his research has been featured by MIT Technology Review and Google Research Blog. He also collaborates closely with Sergey Levine from UC Berkeley/Google Brain and Tim Lillicrap from DeepMind. His publications were presented in NIPS, ICML, ICLR, and ICRA, and he served in the program committee of NIPS 2017 Deep RL Symposium. [Website] [Google Scholar]

Junhyuk Oh - Junhyuk Oh is currently a PhD candidate at the University of Michigan, advised by Prof. Honglak Lee and Prof. Satinder Singh. His research focuses on deep reinforcement learning problems such as action-conditional prediction, dealing with partial observability, generalization, and planning. His work was featured at MIT Technology Review and Daily Mail. He has served as a co-organizer and a program committee of NIPS 2017 Deep RL Symposium. He also interned at DeepMind and Microsoft Research. [Website] [Google Scholar]

Vincent Vanhoucke - Vincent Vanhoucke is a Principal Scientist in the Google Brain Team, and leads Google's robotics research effort (http://g.co/brain/robotics). His research has spanned many areas of artificial intelligence and machine learning, from speech recognition to deep learning, computer vision and robotics. He recently chaired the 2017 Conference on Robot Learning (http://robot-learning.org). He holds a doctorate from Stanford University and a Diplôme d'Ingénieur from the Ecole Centrale Paris. [Website] [Google Scholar]

Oriol Vinyals - Oriol Vinyals is a Sr Staff Research Scientist at Google DeepMind, working in Deep Learning. Prior to joining DeepMind, Oriol was part of the Google Brain team. He holds a Ph.D. in EECS from University of California, Berkeley and is a recipient of the 2016 MIT TR35 innovator award. His research has been featured multiple times at the New York Times, BBC, etc., and his articles have been cited over 15000 times. At DeepMind he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, deep learning and reinforcement learning. [Website] [Google Scholar]

Doina Precup - Doina Precup holds a Canada Research Chair, Tier I in Machine Learning at McGill University, Montreal, Canada, and she currently co-directs the Reasoning and Learning Lab in the School of Computer Science. Prof. Precup also serves as Associate Dean (Research) for the Faculty of Science and Associate Scientific Director of the Healthy Brains for Healthy Lives research program at McGill. Since October 2017, she has led the new DeepMind Montreal research team. Prof. Precup’s research interests are in the area of artificial intelligence and machine learning, with emphasis on reinforcement learning, deep learning, time series analysis, and various applications of these methods. She is a Senior Member of the American Association for Artificial Intelligence and has served as program committee co-chair for the International Conference on Machine Learning (ICML) 2017. She obtained her PhD (2000) from the University of Massachusetts, Amherst, where she was a Fulbright fellow. [Website] [Google Scholar]